There has been an emerging paradigm shift from the era of “internet AI” to “embodied AI,”
where AI algorithms and agents no longer learn from datasets of images, videos or text …

Transformers, originally devised for natural language processing (NLP), have also produced
significant successes in computer vision (CV). Due to their strong expression power …

By transferring knowledge from large, diverse, task-agnostic datasets, modern machine
learning models can solve specific downstream tasks either zero-shot or with small task …

In this work, we present a scalable reinforcement learning method for training multi-task
policies from large offline datasets that can leverage both human demonstrations and …

Semantic navigation is necessary to deploy mobile robots in uncontrolled environments
such as homes or hospitals. Many learning-based approaches have been proposed in …

Vision-and-language navigation (VLN) aims to build autonomous visual agents that follow
instructions and navigate in real scenes. To remember previously visited locations and …

Following language instructions to navigate in unseen environments is a challenging
problem for autonomous embodied agents. The agent not only needs to ground languages …

Understanding crowd motion dynamics is critical to real-world applications, eg, surveillance
systems and autonomous driving. This is challenging because it requires effectively …

In humans, Attention is a core property of all perceptual and cognitive operations. Given our
limited ability to process competing sources, attention mechanisms select, modulate, and …

State-of-the-art approaches to ObjectGoal navigation (ObjectNav) rely on reinforcement
learning and typically require significant computational resources and time for learning. We …